System

Abstract

An object of a system according to an embodiment is to efficiently and effectively learn the contents of a history textbook.SOLUTION: A system according to an embodiment includes a text analysis unit, a summary generation unit, a question answering unit, and an interactive learning unit. The text analysis unit analyzes a text of a history textbook. The summary generation unit extracts an important point from the text analyzed by the text analysis unit and generates a summary. The question answering unit provides an appropriate answer to the user's question based on the summary generated by the summary generation unit. The interactive learner provides an interactive learning experience to the user based on the answer provided by the question answering unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to study the contents of history textbooks efficiently and effectively.

[0005] The system according to the embodiment aims to enable efficient and effective learning of the contents of history textbooks. [Means for solving the problem]

[0006] The system according to the embodiment includes a text analysis unit, a summary generation unit, a question and answer unit, and an interactive learning unit. The text analysis unit analyzes the text of a history textbook. The summary generation unit extracts important points from the text analyzed by the text analysis unit and generates a summary. The question and answer unit provides appropriate answers to user questions based on the summaries generated by the summary generation unit. The interactive learning unit provides the user with an interactive learning experience based on the answers provided by the question and answer unit. [Effects of the Invention]

[0007] The system according to the embodiment enables the content of history textbooks to be studied efficiently and effectively. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A learning support system according to an embodiment of the present invention is a system for efficiently and effectively studying the contents of history textbooks. This system utilizes various functions of Google Gemini to provide text analysis, summary generation, question answering, and an interactive learning experience. As a result, the learning support system enables efficient and effective learning of the contents of history textbooks.

[0029] A learning assistance system according to an embodiment includes a text analysis unit, a summary generation unit, a question and answer unit, and an interactive learning unit. The text analysis unit analyzes text from a history textbook. For example, the text analysis unit analyzes digital text and extracts key points. The text analysis unit can also scan printed text, digitize it using OCR technology, and analyze it. The summary generation unit extracts key points from the text analyzed by the text analysis unit to generate a summary. For example, the summary generation unit uses a generation AI to extract key points from the text and generate a concise summary. The summary generation unit can also generate a summary by extracting important parts of the text based on a model learned by the generation AI. The question and answer unit provides appropriate answers to user questions based on the summaries generated by the summary generation unit. For example, the question and answer unit uses a generation AI to generate answers to user questions. The question and answer unit can also provide appropriate answers to user questions based on a model learned by the generation AI. The interactive learning unit provides an interactive learning experience to the user based on the answers provided by the question and answer unit. For example, the interactive learning unit uses the generation AI to provide quizzes and practice questions to the user. The interactive learning unit can also provide the user with an interactive learning experience based on the model learned by the generation AI. This allows the learning support system according to the embodiment to efficiently and effectively learn the contents of history textbooks. For example, the user can quickly grasp important points and obtain appropriate answers to questions. Furthermore, the interactive learning experience can deepen the user's understanding of history.

[0030] The text analysis unit can analyze the text of a history textbook and generate a detailed summary that includes not only the important points but also related background information and supplementary information. For example, the text analysis unit can analyze the text of a history textbook and generate a detailed summary that includes not only important events and people, but also their background and related events. For example, a summary of the Sengoku period would include the background of the Sengoku daimyo and the social situation of the time. The text analysis unit can also automatically collect related background information and supplementary information and reflect it in the summary. This allows for a deeper understanding of the content of the history textbook.

[0031] The summary generation unit can provide an individually customized summary taking into account the user's learning history and level of understanding. The summary generation unit, for example, analyzes the user's learning history and generates a summary customized according to the user's level of understanding. For example, the summary generation unit can omit content that the user has already learned and provide a summary that focuses on new information. The summary generation unit can also evaluate the user's level of understanding and generate a summary according to the level of understanding. This makes it possible to provide a summary according to the user's learning history and level of understanding.

[0032] The text analysis unit can apply similar text analysis and summary generation functions to teaching materials other than history textbooks. For example, the text analysis unit analyzes the text of a science textbook and summarizes important concepts and experimental results. For example, a summary of Newton's laws of motion includes an explanation of the law and examples of its application. The text analysis unit can also analyze the text of a literary work and generate summaries of important themes and characters. This allows similar functions to be applied to teaching materials other than history textbooks.

[0033] The summary generation unit can automatically generate not only summaries but also related video and audio content. For example, the summary generation unit uses a generation AI to automatically generate video content related to summaries of history textbooks. For example, a summary of the Sengoku period might include a video reenacting a battle between Sengoku daimyo. The summary generation unit can also use a generation AI to automatically generate audio content related to the summary. For example, audio of historical speeches and interviews might be included. This makes it possible to support multimedia learning by automatically generating video and audio content.

[0034] The question answering unit can provide not only answers to user questions but also related additional information and reference materials. For example, the question answering unit uses generative AI to provide answers to user questions along with related additional information and reference materials. For example, in response to the question, "What caused World War II?", a detailed explanation of the causes and related literature are presented. The question answering unit can also provide related website links and reference materials in addition to answers to user questions. This allows for providing more comprehensive information in response to user questions.

[0035] The question answering unit can provide more appropriate answers by taking into account the user's past question history and learning progress. The question answering unit, for example, analyzes the user's past question history and provides appropriate answers that take into account the user's learning progress. For example, it provides related new information based on the content of questions the user has asked in the past. The question answering unit can also evaluate the user's learning progress and provide answers that correspond to the progress. This makes it possible to provide answers that take into account the user's past question history and learning progress.

[0036] The question answering unit can extend the question answering function to fields other than history textbooks, enabling use in a wide range of learning fields. For example, the question answering unit extends the question answering function to mathematics textbooks and provides appropriate answers to user questions. For example, in response to the question, "Please tell me how to prove Pythagoras' theorem," it provides a detailed proof method. The question answering unit can also extend the question answering function to physics textbooks and provide appropriate answers to user questions. This makes it possible to extend the question answering function to fields other than history textbooks.

[0037] The question answering unit can use the generation AI to automatically generate not only question answers but also related quizzes and practice questions. For example, the question answering unit uses the generation AI to automatically generate related quizzes and practice questions along with answers to user questions. For example, in response to the question "Tell me about Napoleon's strategy," a quiz about strategy is provided. The question answering unit can also use the generation AI to automatically generate practice questions related to the answer to the user's question. This makes it possible to provide not only question answers but also related quizzes and practice questions.

[0038] The interactive learning unit can add a personalization function to reflect the user's interests and concerns. The interactive learning unit can add a personalization function to the interactive learning experience to reflect the user's interests and concerns, for example, by providing learning content related to topics in which the user has shown interest. The interactive learning unit can also recommend content based on the user's past learning history in accordance with the user's interests and concerns. This makes it possible to provide an interactive learning experience that reflects the user's interests and concerns.

[0039] The interactive learning unit can provide an interactive learning experience for teaching materials other than history textbooks, enabling use in a wide range of learning fields. For example, the interactive learning unit can provide an interactive learning experience for a geography textbook, allowing users to gain a deeper understanding of the geography content. For example, the interactive learning unit can provide quizzes on geographical features and landforms. The interactive learning unit can also provide an interactive learning experience for a biology textbook, allowing users to gain a deeper understanding of the biology content. This makes it possible to provide an interactive learning experience for teaching materials other than history textbooks.

[0040] The interactive learning unit can use the generative AI to provide not only an interactive learning experience but also related simulation and virtual reality content. For example, the interactive learning unit can use the generative AI to provide related simulation content in addition to an interactive learning experience. For example, content that simulates a historical battle scene can be provided. The interactive learning unit can also use the generative AI to provide virtual reality content. For example, virtual reality technology can be used to provide an experience where a user virtually visits a historical location. This can provide a deeper learning experience by providing simulation and virtual reality content.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The learning assistance system may further include a speech recognition unit. The speech recognition unit allows a user to input questions by voice, and the question-answering unit analyzes the voice and provides appropriate answers. For example, if a user asks by voice, "Tell me about Napoleon's strategy," the speech recognition unit converts the question into text, and the question-answering unit generates an answer. The speech recognition unit can also learn the user's pronunciation and accent to provide more accurate speech recognition. This allows the user to input questions by voice without using their hands and advance their learning.

[0043] The learning support system may further include a translation unit. The translation unit translates the contents of a history textbook into multiple languages, providing learning opportunities for users who speak different languages. For example, an English history textbook may be translated into Japanese, allowing Japanese-speaking users to study. Furthermore, even if a user inputs a question in a different language, the translation unit can translate the question, and the question answering unit can provide an appropriate answer. This allows a learning support system that is compatible with users who speak different languages ​​to be provided.

[0044] The learning support system can further include a gamification unit. The gamification unit provides learning content in a game format to increase the user's motivation to learn. For example, important historical events may be presented in the form of a quiz, allowing the user to earn points for each correct answer. The gamification unit can also provide a mechanism for users to level up and earn badges according to their learning progress. This allows users to enjoy learning.

[0045] The learning support system can further include a virtual assistant unit. The virtual assistant unit provides real-time support for any questions or problems that arise while the user is studying. For example, if the user wants to know more about a particular historical event, the virtual assistant can provide that information. The virtual assistant unit can also monitor the user's learning progress and provide study advice and reminders at appropriate times. This allows the user to continue studying while always receiving support.

[0046] The learning support system may further include a social learning unit. The social learning unit provides a platform for users to discuss and share information about learning content. For example, a forum may be provided where users can exchange opinions about a particular historical event. The social learning unit may also provide a function that allows users to create study groups and study together. This allows users to interact with other learners while studying.

[0047] The learning support system can further include a data analysis unit. The data analysis unit analyzes the user's learning data and evaluates the effectiveness of the learning. For example, the data analysis unit can visualize the user's learning progress and level of understanding in graphs and charts, allowing the user to understand their own learning situation. The data analysis unit can also analyze the user's learning patterns and suggest effective learning methods. This allows the user to objectively evaluate their learning situation and promote effective learning.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The text analysis unit analyzes the text of the history textbook. For example, it analyzes the digital text and extracts key points. It can also scan the printed text, digitize it using OCR technology, and analyze it. Step 2: The summary generation unit extracts important points from the text analyzed by the text analysis unit and generates a summary. For example, the generation AI can be used to extract the main points of the text and generate a concise summary. Alternatively, the generation AI can extract important parts of the text and generate a summary based on the model it has learned. Step 3: The question answering unit provides an appropriate answer to the user's question based on the summary generated by the summary generation unit. For example, the answer to the user's question is generated using a generation AI. The generation AI can also provide an appropriate answer to the user's question based on the model it has learned. Step 4: The interactive learning unit provides the user with an interactive learning experience based on the answers provided by the question-answering unit. For example, the generation AI can be used to provide the user with quizzes and practice questions. The generation AI can also provide the user with an interactive learning experience based on the model it has learned.

[0050] (Example 2) A learning support system according to an embodiment of the present invention is a system for efficiently and effectively studying the contents of history textbooks. This system utilizes various functions of Google Gemini to provide text analysis, summary generation, question answering, and an interactive learning experience. As a result, the learning support system enables efficient and effective learning of the contents of history textbooks.

[0051] A learning assistance system according to an embodiment includes a text analysis unit, a summary generation unit, a question and answer unit, and an interactive learning unit. The text analysis unit analyzes text from a history textbook. For example, the text analysis unit analyzes digital text and extracts key points. The text analysis unit can also scan printed text, digitize it using OCR technology, and analyze it. The summary generation unit extracts key points from the text analyzed by the text analysis unit to generate a summary. For example, the summary generation unit uses a generation AI to extract key points from the text and generate a concise summary. The summary generation unit can also generate a summary by extracting important parts of the text based on a model learned by the generation AI. The question and answer unit provides appropriate answers to user questions based on the summaries generated by the summary generation unit. For example, the question and answer unit uses a generation AI to generate answers to user questions. The question and answer unit can also provide appropriate answers to user questions based on a model learned by the generation AI. The interactive learning unit provides an interactive learning experience to the user based on the answers provided by the question and answer unit. For example, the interactive learning unit uses the generation AI to provide quizzes and practice questions to the user. The interactive learning unit can also provide the user with an interactive learning experience based on the model learned by the generation AI. This allows the learning support system according to the embodiment to efficiently and effectively learn the contents of history textbooks. For example, the user can quickly grasp important points and obtain appropriate answers to questions. Furthermore, the interactive learning experience can deepen the user's understanding of history.

[0052] The text analysis unit can analyze the text of a history textbook and generate a detailed summary that includes not only the important points but also related background information and supplementary information. For example, the text analysis unit can analyze the text of a history textbook and generate a detailed summary that includes not only important events and people, but also their background and related events. For example, a summary of the Sengoku period would include the background of the Sengoku daimyo and the social situation of the time. The text analysis unit can also automatically collect related background information and supplementary information and reflect it in the summary. This allows for a deeper understanding of the content of the history textbook.

[0053] The summary generation unit can provide an individually customized summary taking into account the user's learning history and level of understanding. The summary generation unit, for example, analyzes the user's learning history and generates a summary customized according to the user's level of understanding. For example, the summary generation unit can omit content that the user has already learned and provide a summary that focuses on new information. The summary generation unit can also evaluate the user's level of understanding and generate a summary according to the level of understanding. This makes it possible to provide a summary according to the user's learning history and level of understanding.

[0054] The summary generation unit can use the emotion estimation function to prioritize inclusion in the summary of content that is likely to interest the user. The summary generation unit, for example, uses the emotion estimation function to prioritize inclusion in the summary of content that is likely to interest the user. For example, the summary generation unit generates a summary that focuses on historical events or people that the user has shown interest in. The summary generation unit can also analyze the user's emotional response and reflect content that is of interest to the user in the summary. This allows content that is of interest to be prioritized in the summary.

[0055] The text analysis unit can apply similar text analysis and summary generation functions to teaching materials other than history textbooks. For example, the text analysis unit analyzes the text of a science textbook and summarizes important concepts and experimental results. For example, a summary of Newton's laws of motion includes an explanation of the law and examples of its application. The text analysis unit can also analyze the text of a literary work and generate summaries of important themes and characters. This allows similar functions to be applied to teaching materials other than history textbooks.

[0056] The summary generation unit can automatically generate not only summaries but also related video and audio content. For example, the summary generation unit uses a generation AI to automatically generate video content related to summaries of history textbooks. For example, a summary of the Sengoku period might include a video reenacting a battle between Sengoku daimyo. The summary generation unit can also use a generation AI to automatically generate audio content related to the summary. For example, audio of historical speeches and interviews might be included. This makes it possible to support multimedia learning by automatically generating video and audio content.

[0057] The summary generation unit can use the emotion estimation function to analyze the emotional response of the user when reading the summary and adjust the summary content to elicit a positive response. For example, the summary generation unit can use the emotion estimation function to analyze the emotional response of the user when reading the summary in real time and adjust the summary content to elicit a positive response. For example, the summary generation unit can highlight topics that the user is interested in. The summary generation unit can also dynamically adjust the summary content based on the user's emotional response. This makes it possible to analyze the user's emotional response and provide summary content that elicits a positive response.

[0058] The question answering unit can provide not only answers to user questions but also related additional information and reference materials. For example, the question answering unit uses generative AI to provide answers to user questions along with related additional information and reference materials. For example, in response to the question, "What caused World War II?", a detailed explanation of the causes and related literature are presented. The question answering unit can also provide related website links and reference materials in addition to answers to user questions. This allows for providing more comprehensive information in response to user questions.

[0059] The question answering unit can provide more appropriate answers by taking into account the user's past question history and learning progress. The question answering unit, for example, analyzes the user's past question history and provides appropriate answers that take into account the user's learning progress. For example, it provides related new information based on the content of questions the user has asked in the past. The question answering unit can also evaluate the user's learning progress and provide answers that correspond to the progress. This makes it possible to provide answers that take into account the user's past question history and learning progress.

[0060] The question answering unit can use the emotion estimation function to analyze the user's emotional response to a question and generate an answer that elicits positive emotions. For example, the question answering unit can use the emotion estimation function to analyze the user's emotional response to a question in real time and generate an answer that elicits positive emotions. For example, the question answering unit can provide an answer that focuses on a topic in which the user has shown interest. The question answering unit can also dynamically adjust the content of the answer based on the user's emotional response. This makes it possible to analyze the user's emotional response and provide an answer that elicits positive emotions.

[0061] The question answering unit can extend the question answering function to fields other than history textbooks, enabling use in a wide range of learning fields. For example, the question answering unit extends the question answering function to mathematics textbooks and provides appropriate answers to user questions. For example, in response to the question, "Please tell me how to prove Pythagoras' theorem," it provides a detailed proof method. The question answering unit can also extend the question answering function to physics textbooks and provide appropriate answers to user questions. This makes it possible to extend the question answering function to fields other than history textbooks.

[0062] The question answering unit can use the generation AI to automatically generate not only question answers but also related quizzes and practice questions. For example, the question answering unit uses the generation AI to automatically generate related quizzes and practice questions along with answers to user questions. For example, in response to the question "Tell me about Napoleon's strategy," a quiz about strategy is provided. The question answering unit can also use the generation AI to automatically generate practice questions related to the answer to the user's question. This makes it possible to provide not only question answers but also related quizzes and practice questions.

[0063] The question answering unit can use the emotion estimation function to monitor the user's emotional response to a question in real time and provide an optimal answer. The question answering unit can, for example, use the emotion estimation function to monitor the user's emotional response to a question in real time and provide an optimal answer. For example, the question answering unit can provide an answer that focuses on a topic in which the user has shown interest. The question answering unit can also dynamically adjust the content of the answer based on the user's emotional response. This makes it possible to monitor the user's emotional response in real time and provide an optimal answer.

[0064] The interactive learning unit can add a personalization function to reflect the user's interests and concerns. The interactive learning unit can add a personalization function to the interactive learning experience to reflect the user's interests and concerns, for example, by providing learning content related to topics in which the user has shown interest. The interactive learning unit can also recommend content based on the user's past learning history in accordance with the user's interests and concerns. This makes it possible to provide an interactive learning experience that reflects the user's interests and concerns.

[0065] The interactive learning unit can use the emotion estimation function to analyze the user's emotional response and provide interactive content that elicits positive emotions. The interactive learning unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time and provide interactive content that elicits positive emotions. For example, the interactive learning unit can provide a quiz related to a topic in which the user has shown interest. The interactive learning unit can also dynamically adjust the interactive content based on the user's emotional response. This makes it possible to analyze the user's emotional response and provide interactive content that elicits positive emotions.

[0066] The interactive learning unit can provide an interactive learning experience for teaching materials other than history textbooks, enabling use in a wide range of learning fields. For example, the interactive learning unit can provide an interactive learning experience for a geography textbook, allowing users to gain a deeper understanding of the geography content. For example, the interactive learning unit can provide quizzes on geographical features and landforms. The interactive learning unit can also provide an interactive learning experience for a biology textbook, allowing users to gain a deeper understanding of the biology content. This makes it possible to provide an interactive learning experience for teaching materials other than history textbooks.

[0067] The interactive learning unit can use the generative AI to provide not only an interactive learning experience but also related simulation and virtual reality content. For example, the interactive learning unit can use the generative AI to provide related simulation content in addition to an interactive learning experience. For example, content that simulates a historical battle scene can be provided. The interactive learning unit can also use the generative AI to provide virtual reality content. For example, virtual reality technology can be used to provide an experience where a user virtually visits a historical location. This can provide a deeper learning experience by providing simulation and virtual reality content.

[0068] The interactive learning unit can use the emotion estimation function to monitor the user's emotional responses in real time and provide an optimal interactive learning experience. The interactive learning unit can, for example, use the emotion estimation function to monitor the user's emotional responses in real time and provide an optimal interactive learning experience. For example, the interactive learning unit can provide a quiz related to a topic in which the user has shown interest. The interactive learning unit can also dynamically adjust the learning experience based on the user's emotional responses. This allows the user's emotional responses to be monitored in real time and provide an optimal interactive learning experience.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The learning assistance system may further include a speech recognition unit. The speech recognition unit allows a user to input questions by voice, and the question-answering unit analyzes the voice and provides appropriate answers. For example, if a user asks by voice, "Tell me about Napoleon's strategy," the speech recognition unit converts the question into text, and the question-answering unit generates an answer. The speech recognition unit can also learn the user's pronunciation and accent to provide more accurate speech recognition. This allows the user to input questions by voice without using their hands and advance their learning.

[0071] The learning support system may further include a translation unit. The translation unit translates the contents of a history textbook into multiple languages, providing learning opportunities for users who speak different languages. For example, an English history textbook may be translated into Japanese, allowing Japanese-speaking users to study. Furthermore, even if a user inputs a question in a different language, the translation unit can translate the question, and the question answering unit can provide an appropriate answer. This allows a learning support system that is compatible with users who speak different languages ​​to be provided.

[0072] The learning support system can further include a gamification unit. The gamification unit provides learning content in a game format to increase the user's motivation to learn. For example, important historical events may be presented in the form of a quiz, allowing the user to earn points for each correct answer. The gamification unit can also provide a mechanism for users to level up and earn badges according to their learning progress. This allows users to enjoy learning.

[0073] The learning support system can also use the emotion estimation function to provide feedback to maintain the user's motivation to learn. For example, if the user is losing interest in learning, the emotion estimation function can detect this state and display an encouraging message to increase motivation. Also, if the user has positive emotions toward learning, the system can provide praise or rewards to reinforce those emotions. This can maintain the user's motivation to learn and support effective learning.

[0074] The learning support system can further include a virtual assistant unit. The virtual assistant unit provides real-time support for any questions or problems that arise while the user is studying. For example, if the user wants to know more about a particular historical event, the virtual assistant can provide that information. The virtual assistant unit can also monitor the user's learning progress and provide study advice and reminders at appropriate times. This allows the user to continue studying while always receiving support.

[0075] The learning support system can also use its emotion estimation function to monitor the user's stress level and provide appropriate relaxation content. For example, if a user is feeling high stress while studying, the emotion estimation function can detect this state and provide relaxation music or deep breathing guidance. It can also display a message encouraging the user to take a moderate break so that the user can continue studying in a relaxed state. This reduces the user's stress and supports effective learning.

[0076] The learning support system can also use the emotion estimation function to adjust the difficulty of learning content according to the user's emotions. For example, if the user is feeling anxious or stressed about learning, the emotion estimation function can detect this state and provide content with a lower level of difficulty. Also, if the user has positive emotions about learning, the system can provide more challenging content with a higher level of difficulty. This makes it possible to provide an optimal learning experience according to the user's emotions.

[0077] The learning support system may further include a social learning unit. The social learning unit provides a platform for users to discuss and share information about learning content. For example, a forum may be provided where users can exchange opinions about a particular historical event. The social learning unit may also provide a function that allows users to create study groups and study together. This allows users to interact with other learners while studying.

[0078] The learning support system can also use an emotion estimation function to suggest a study schedule based on the user's emotions. For example, if the user feels tired from studying, the emotion estimation function will detect this state and suggest a schedule that includes breaks. Also, if the user is highly motivated to study, it can suggest time periods when the user can concentrate on studying. This makes it possible to provide an optimal study schedule based on the user's emotions.

[0079] The learning support system can further include a data analysis unit. The data analysis unit analyzes the user's learning data and evaluates the effectiveness of the learning. For example, the data analysis unit can visualize the user's learning progress and level of understanding in graphs and charts, allowing the user to understand their own learning situation. The data analysis unit can also analyze the user's learning patterns and suggest effective learning methods. This allows the user to objectively evaluate their learning situation and promote effective learning.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The text analysis unit analyzes the text of the history textbook. For example, it analyzes the digital text and extracts key points. It can also scan the printed text, digitize it using OCR technology, and analyze it. Step 2: The summary generation unit extracts important points from the text analyzed by the text analysis unit and generates a summary. For example, the generation AI can be used to extract the main points of the text and generate a concise summary. Alternatively, the generation AI can extract important parts of the text and generate a summary based on the model it has learned. Step 3: The question answering unit provides an appropriate answer to the user's question based on the summary generated by the summary generation unit. For example, the answer to the user's question is generated using a generation AI. The generation AI can also provide an appropriate answer to the user's question based on the model it has learned. Step 4: The interactive learning unit provides the user with an interactive learning experience based on the answers provided by the question-answering unit. For example, the generation AI can be used to provide the user with quizzes and practice questions. The generation AI can also provide the user with an interactive learning experience based on the model it has learned.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a text analysis unit that analyzes the text of history textbooks; a summary generation unit that extracts important points from the text analyzed by the text analysis unit and generates a summary; a question answering unit that provides appropriate answers to user questions based on the summaries generated by the summary generating unit; an interactive learning unit that provides an interactive learning experience to a user based on answers provided by the question and answer unit. A system characterized by:

2. The text analysis unit Analyze the text of a history textbook and generate a detailed summary that includes not only the key points but also relevant background and supporting information.

2. The system of claim 1.

3. The summary generation unit Automatically generate summaries as well as related video and audio content 2. The system of claim 1.

4. The question answering unit Providing not only the answer to the user's question but also additional related information and references 2. The system of claim 1.

5. The interactive learning unit Providing a customized, interactive learning experience tailored to the user's learning progress and level of understanding 2. The system of claim 1.

6. The summary generation unit Using an emotion estimation function, content that is likely to interest the user is preferentially included in the summary.

2. The system of claim 1.

7. The question answering unit Using emotion estimation capabilities, the emotional response of the user to the question is analyzed to generate an answer that elicits positive emotions.

2. The system of claim 1.

8. The interactive learning unit Using emotion estimation function, the emotional response of the user is analyzed and interactive content that elicits positive emotions is provided.

2. The system of claim 1.

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